Estimating attributes for the classification of adaptive loop filtering based on projection-slice theorem

ABSTRACT

A method, computer program, and computer system is provided for video coding. Video data comprising sub-blocks corresponding to neighborhood data is received. AC projections are computed based on at least a portion of the neighborhood data, and AC projections in the same direction are aggregated. One or more AC energy indices and one or more directionality indices are computed based on the aggregated AC projections. A class index is computed based on the computed AC energy and directionality indices. The video data is decoded based on the computed class index.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority based on U.S. Provisional ApplicationNo. 63/001,170 (filed Mar. 27, 2020), the entirety of which is hereinincorporated by reference.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to video coding.

BACKGROUND

AVS HPM-6 inherited the adaptive loop filter (ALF) from AVS 2.0. TheHPM-6 ALF is a region-based ALF. It divides a reconstructed picture,which is an output after the deblocking and SAO process, into 16regions. Each region could be filtered by a FIR filter to improvepicture quality.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forvideo coding. According to one aspect, a method for video coding isprovided. The method may include receiving video data comprisingsub-blocks corresponding to neighborhood data AC projections arecomputed based on at least a portion of the neighborhood data, and ACprojections in the same direction are aggregated. One or more AC energyindices and one or more directionality indices are computed based on theaggregated AC projections. A class index is computed based on thecomputed AC energy and directionality indices. The video data is decodedbased on the computed class index.

According to another aspect, a computer system for video coding isprovided. The computer system may include one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving video data comprising sub-blocks corresponding toneighborhood data AC projections are computed based on at least aportion of the neighborhood data, and AC projections in the samedirection are aggregated. One or more AC energy indices and one or moredirectionality indices are computed based on the aggregated ACprojections. A class index is computed based on the computed AC energyand directionality indices. The video data is decoded based on thecomputed class index.

According to yet another aspect, a computer readable medium for videocoding is provided. The computer readable medium may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving video data comprising sub-blocks corresponding toneighborhood data AC projections are computed based on at least aportion of the neighborhood data, and AC projections in the samedirection are aggregated. One or more AC energy indices and one or moredirectionality indices are computed based on the aggregated ACprojections. A class index is computed based on the computed AC energyand directionality indices. The video data is decoded based on thecomputed class index.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2A is a block diagram of an adaptive loop filter (ALF) for videocoding, according to at least one embodiment;

FIG. 2B is a diagram of a diagram of subsampling positions for samplescorresponding to one or more gradient directions, according to at leastone embodiment;

FIG. 3 is a diagram of a diagram of directional AC energy indicators,according to at least one embodiment;

FIG. 4 is an operational flowchart illustrating the steps carried out bya program for video coding, according to at least one embodiment;

FIG. 5 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 7 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 6, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to media processing. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, refine a chroma component of video data using a lumacomponent of the video data. Therefore, some embodiments have thecapacity to improve the field of computing by allowing for improvedcoding efficiency in AVS3 HPM-6 by replacing the region-based loopfiltering by block-based loop filtering, where the filter is selectedusing features derived from the projection-slice theorem. Instead ofusing region based adaptive loop filter, the method, computer system,and computer readable medium disclosed herein apply block based adaptiveloop filter, where the filter may be switched at 4×4 block levels. Incontrast to VVC, the switching method is not gradient based. Theswitching method disclosed herein is based on the projection-slicetheorem.

As previously described, AVS HPM-6 inherited the adaptive loop filter(ALF) from AVS 2.0. The HPM-6 ALF is a region-based ALF. It divides areconstructed picture, which is an output after the deblocking and SAOprocess, into 16 regions. Each region could be filtered by a FIR filterto improve picture quality. However, a maximum of 16 filters could beencoded in the bitstream for ALF. It may be advantageous, therefore, toreduce the number of filters encoded in the bitstream by having adjacentregions share the same filter.

In contrast to HPM-6, the VVC VTM-8.0 adopted the block-based adaptiveloop filter (BALF), where the FIR filter could be switched at 4×4 blocklevel. The filter for a 4×4 block is selected using features based on 1DLaplacian of the intensity in an 8×8 neighbourhood of the 4×4 block. Toimprove the coding efficiency of HPM-6, block adaptive loop filteringmay be applied to HPM-6 without using 1D Laplacian for the AVS-3standard.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that allows for refinement of a chroma component ofvideo data using a luma component of the video data. Referring now toFIG. 1, a functional block diagram of a networked computer environmentillustrating a media processing system 100 (hereinafter “system”) forvideo coding. It should be appreciated that FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 5 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 6 and 7. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for video coding is enabledto run an Video Coding Program 116 (hereinafter “program”) that mayinteract with a database 112. The Video Coding Program method isexplained in more detail below with respect to FIG. 4. In oneembodiment, the computer 102 may operate as an input device including auser interface while the program 116 may run primarily on servercomputer 114. In an alternative embodiment, the program 116 may runprimarily on one or more computers 102 while the server computer 114 maybe used for processing and storage of data used by the program 116. Itshould be noted that the program 116 may be a standalone program or maybe integrated into a larger video coding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2A, a block diagram of an exemplary adaptive loopfilter (ALF filter) 200 with block-based filter adaption is depicted.The ALF filter 200 may include a chroma component 202 and a lumacomponent 204. For the luma component 204, one filter among twenty-fivefilters may be selected for each 4×4 block, based on the direction andactivity of local gradients. Two diamond filter shapes may be used. A7-by-7 diamond shape 202 may be applied for luma components and a 5-by-5diamond shape may be applied for chroma components.

The ALF filter 200 may make use of luma sample values to refine eachchroma component by applying a linear, diamond shaped filter to the lumachannel for each chroma component. The filter coefficients aretransmitted may be scaled by a factor of 2¹⁰ and rounded for fixed pointrepresentation. The application of the filters may be controlled on avariable block size and signalled by a context-coded flag received foreach block of samples. The block size along with an ALF enabling flagmay be received at the slice-level for each chroma component.

For the chroma components in a picture, a single set of ALF coefficientsC0-C6 may is applied for the chroma component 202.

For the luma component 204, each 4-by-4 block may be categorized intoone of twenty-five primary classes. Each primary class is furtherdivided into four secondary classes, with a total of 100 classes. TheALF may be switched based on the classification of the 4×4 block. Theprimary classification is related to the directionality and activity ofthe 4×4 block. The secondary classification is related to geometricaltransformation of a filter by a rotation of 90 degree and/or reflectionaround a vertical axis. A classification index C may be derived based onits directionality D and a quantized value of activity Â, such thatC=5D+Â. To calculate D and Â, gradients of the horizontal, vertical andtwo diagonal direction may be calculated using 1-D Laplacian:g _(v)=Σ_(k=i−2) ^(i+3)Σ_(l=j−2) ^(j+3) V _(k,l) ,V_(k,l)=|2R(k,l)−R(k,l−1)−R(k,l+1)|  (1)g _(h)=Σ_(k=i−2) ^(i+3)Σ_(l=j−2) ^(j+3) H _(k,l) ,H_(k,l)=|2R(k,l)−R(k−1,l)−R(k+1,l)|  (2)g _(d1)=Σ_(k=i−2) ^(i+3)Σ_(l=j−3) ^(j+3) D1_(k,l),D1_(k,l)=|2R(k,l)−R(k−1,l−1)−R(k+1,l+1)|  (3)g _(d2)=Σ_(k=i−2) ^(i+3)Σ_(j=j−2) ^(j+3) D2_(k,l),D2_(k,l)=|2R(k,l)−R(k−1,l−1)−R(k+1,l+1)|  (4)where indices i and j may refer to the coordinates of the upper leftsample within the 4×4 block and R(I, j) may indicate a reconstructedsample at coordinate (I, j). To reduce the complexity of blockclassification, the subsampled 1-D Laplacian calculation is applied. Thesame subsampled positions may be used for gradient calculation of alldirections.

Maximum and minimum D values of the gradients of horizontal and verticaldirections may be set as:g _(h,v) ^(max)=max(g _(h) ,g _(v)),  (5)g _(h,v) ^(min)=min(g _(h) ,g _(v)),  (6)The maximum and minimum values of the gradient of two diagonaldirections may be set as:g _(d0,d1) ^(max)=max(g _(d0) ,g _(d1)),g _(d0,d1) ^(min)=min(g _(d0) ,g_(d1))  (7)

To derive the value of the directionality D, the maximum and minimumvalues may be compared against each other and with two thresholds t₁ andt₂. If both g_(h,v) ^(max)≤t₁·g_(h,v) ^(min) and g_(d0,d1)^(max)≤t₁·g_(d0,d1) ^(min) are true, D is set to 0. If g_(h,v)^(max)/g_(h,v) ^(min)>g_(d0,d1) ^(max)/g_(d0,d1) ^(min), then if g_(h,v)^(max)>t₂. g_(h,v) ^(min), D may be set to 2; otherwise D may be setto 1. If g_(h,v) ^(max)/g_(h,v) ^(min)≤g_(d0,d1) ^(max)/g_(d0,d1)^(min), then if g_(d0,d1) ^(max)>t₂·g_(d0,d1) ^(min), D may be set to 4;otherwise D may be set to 3.

The activity value may be calculated as:

$\begin{matrix}{{A = {{\sum\limits_{k = {i - 2}}^{i + 3}{\sum\limits_{l = {j - 2}}^{j + 3}\left( {V_{k,l} + H_{k,l}} \right)}} = {g_{v} + g_{h}}}}.} & (8)\end{matrix}$A may be further quantized to the range of 0 to 4, inclusively. Thequantized value may be denoted as Â. For chroma components in a picture,no classification method may need be applied.

Referring now to FIG. 2B, a diagram 200B of exemplary subsamplingpositions for samples corresponding to one or more gradient directionsis depicted. The subsampling positions may include positions for asample 302A corresponding to a vertical gradient, a sample 302Bcorresponding to a horizontal gradient, a sample 302C corresponding to afirst diagonal gradient, and a sample 302D corresponding to a seconddiagonal gradient. The subsampling positions V, H, D1, and D2 maycorrespond to the vertical, horizontal, first diagonal, and seconddiagonal gradients, respectively.

Referring now to FIG. 3, a diagram 300 of exemplary directional ACenergy indicators is depicted. The directional AC energy indicators areclassified into 7 classes. The directional AC indicator is interpretedas a vector with directions along the direction of the arrows. Thelabels are the classifications of the AC energy indicators based on theamplitude and direction of the directional AC indicator.

The Video Coding Program 116 (FIG. 1) computes the class index of a 4×4block from an 8×8 neighborhood with the 4×4 block at the center of the8×8 neighborhood. The ALF filter for the 4×4 block is switched accordingto the classification. The Video Coding Program 116 may compute thehorizontal, vertical, 45 degree and 135 degree directional ACprojections of 2×2 subblocks in the 8×8 neighborhood based on theprojection-slice theorem. The L1 norm of the projections at a givenangle of the 2×2 blocks in the 8×8 neighborhood are summed together asthe directional AC energy indicator of the 4×4 block at the angleorthogonal to the given angle, as in the projection-slice theorem. Thenthe directional AC energy indicator of the 4×4 block in the vertical,horizontal, 135 degree and 45 degree are used to derive thedirectionality index D and energy index E. The classification index Cfor the 4×4 block class is derived based on its directionality index Dand the energy index E as: C=7D+E.

The AC projection of a 2×2 block at a given angle is computed by firstremoving the DC component from the 2×2 block and then summing the pixelvalues along in a line oriented at a given angle as in theprojection-slice theorem. The absolute value of the 2×2 AC projectionswith the same direction are added together to form the directional ACenergy indicator at an angle orthogonal to the direction.

Classification of a 4×4 block for the selection of adaptive loop filteris based on the amount of the AC energy of the block and thedirectionality of the AC energy. For the classification of the amount ofthe AC energy, the AC energy index is computed from quantized value ofthe sum of the directional AC energy indicator in vertical andhorizontal direction. In one embodiment, the quantized value has aninteger value of 0 to 4 inclusive.

For the classification of the directionality of the AC energy, thedirectional AC energy indicator in vertical, horizontal, 135 degree and45 degree are input to compute the directionality index D which classifythe directional AC energy indicators into 7 classes, includingnon-directional, strongly horizontal, horizontal-up, horizontal-down,strongly vertical, vertical-left, and vertical-right.

In one embodiment, the directional AC energy may be classified in thefollowing order. The directional AC energy is first classified if it isnon-direction, when the maximum of horizontal and vertical energy isabout the same as the maximum of the 45 degree and 135 degree energy. Ifthe AC energy is directional, it is classified as strongly horizontal orstrongly vertical. If the AC energy is not strongly horizontal/vertical,and if the horizontal AC energy is larger than the vertical AC energy,the directional AC energy is classified as horizontal-up if the 45degree energy is larger than the 135 degree energy. Otherwise it isclassified as horizontal-down. If the horizontal AC energy is not largerthan the vertical AC energy, if the vertical AC energy is larger than orequal to the horizontal energy, the directional AC energy is classifiedas vertical-left if the 45 degree energy is larger than the 135 degreeenergy. Otherwise, it is classified as vertical-right.

According to one or more embodiments, the directional AC energyindicators can be classified in N directions and includes thenon-directional class, where N is an integer.

According to one or more embodiments, the block classification includesan on/off class for the filtering of the block based on the directionalAC energy indicators. In one embodiment, the block size is 4×4.

According to one or more embodiments, the number of AC energy class canbe classified in N classes with AC energy index in the range of 0 to N−1inclusively.

According to one or more embodiments, the block classification can alsobe based on the ratio of horizontal and vertical directional AC energyindicators and/or the ratio of the 45-degree and/or 135-degreedirectional AC energy indicators.

According to one or more embodiments, the block classification is basedon AC projections of N×N sub-blocks where could be 2, 4, or 8.

According to one or more embodiments, the block classification is basedon projections N×N sub-blocks without DC removal where N could be 2, 4,or 8.

Referring now to FIG. 4, an operational flowchart 400 illustrating thesteps carried out by a program for video coding is depicted. In someimplementations, one or more process blocks of FIG. 4 may be performedby the computer 102 (FIG. 1) and the server computer 114 (FIG. 1). Insome implementations, one or more process blocks of FIG. 4 may beperformed by another device or a group of devices separate from orincluding the computer 102 and the server computer 114.

At 402, the method 400 includes receiving video data comprisingsub-blocks corresponding to neighborhood data.

At 404, the method 400 includes computing AC projection based on atleast a portion of the neighborhood data.

At 406, the method 400 includes aggregating the AC projections in thesame direction.

At 408, the method 400 includes computing one or more AC energy indicesand one or more directionality indices based on the aggregated ACprojections.

At 410, the method 400 includes computing a class index based on thecomputed AC energy and directionality indices.

At 412, the method 400 includes decoding the video data based on thecomputed class index.

It may be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 5 is a block diagram 500 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 5 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Video Coding Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 5, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Video Coding Program 116 (FIG. 1) can bestored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theVideo Coding Program 116 (FIG. 1) on the server computer 114 (FIG. 1)can be downloaded to the computer 102 (FIG. 1) and server computer 114from an external computer via a network (for example, the Internet, alocal area network or other, wide area network) and respective networkadapters or interfaces 836. From the network adapters or interfaces 836,the software program 108 and the Video Coding Program 116 on the servercomputer 114 are loaded into the respective hard drive 830. The networkmay comprise copper wires, optical fibers, wireless transmission,routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 6, illustrative cloud computing environment 600 isdepicted. As shown, cloud computing environment 600 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 600 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 600 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 7, a set of functional abstraction layers 700 providedby cloud computing environment 600 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Video Coding 96. Video Coding 96 mayallow for encoding and decoding of video data with constrained filteringcoefficients to limit the dynamic range of the video data.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of coding video data, executable by aprocessor, comprising: receiving video data comprising sub-blockscorresponding to neighborhood data; computing an AC projection of eachsub-block of the sub-blocks at an angle by removing a DC component froma sub-block and summing pixel values along in a line oriented at theangle; aggregating AC projections of the sub-blocks in the samedirection that is orthogonal to the angle, by summing absolute values ofthe AC projections in the same direction; computing AC energy indicesand directionality indices based on the aggregated AC projections in aplurality of directions, an AC energy index at a given angle being basedon aggregated AC projections in a direction that is orthogonal to thegiven angle; computing a class index based on the computed AC energy anddirectionality indices; and decoding the video data based on thecomputed class index.
 2. The method of claim 1, wherein a number of thedirectionality indices is the same as a number of the plurality ofdirections and a non-directional class.
 3. The method of claim 2,wherein a number of AC energy indices corresponds to the number ofplurality of directions.
 4. The method of claim 1, further comprisingclassifying the sub-blocks based on an on/off class for filtering of thesub-block based on the directional AC energy indicators.
 5. The methodof claim 4, wherein the sub-block classification is based on a ratio ofhorizontal and vertical directional AC energy indices and/or a ratio of45-degree and 135-degree directional AC energy indicators.
 6. The methodof claim 4, wherein the sub-block classification is based on ACprojections associated with the sub-blocks.
 7. The method of claim 4,wherein the block classification is based on projections associated withthe sub-blocks without DC removal.
 8. A computer system for videocoding, the computer system comprising: one or more computer-readablenon-transitory storage media configured to store computer program code;and one or more computer processors configured to access said computerprogram code and operate as instructed by said computer program code,said computer program code including: receiving code configured to causethe one or more computer processors to receive video data comprisingsub-blocks corresponding to neighborhood data; first computing codeconfigured to cause the one or more computer processors to compute an ACprojection of each sub-block of the sub-blocks at an angle by removing aDC component from a sub-block and summing pixel values along in a lineoriented at the angle; aggregating code configured to cause the one ormore computer processors to aggregate AC projections of the sub-blocksin the same direction that is orthogonal to the angle, by summingabsolute values of the AC projections in the same direction; secondcomputing code configured to cause the one or more computer processorsto compute AC energy indices and directionality indices based on theaggregated AC projections in a plurality of directions, an AC energyindex at a given angle being based on aggregated AC projections in adirection that is orthogonal to the given angle; third computing codeconfigured to cause the one or more computer processors to compute aclass index based on the computed AC energy and directionality indices;and decoding code configured to cause the one or more computerprocessors to decode the video data based on the computed class index.9. The computer system of claim 8, wherein a number of thedirectionality indices is the same as a number of the plurality ofdirections and a non-directional class.
 10. The computer system of claim9, wherein a number of AC energy indices corresponds to the number ofdirections.
 11. The computer system of claim 8, further comprisingclassifying the sub-blocks based on an on/off class for filtering of thesub-block based on the directional AC energy indicators.
 12. Thecomputer system of claim 11, wherein the sub-block classification isbased on a ratio of horizontal and vertical directional AC energyindices and/or a ratio of 45-degree and 135-degree directional AC energyindicators.
 13. The computer system of claim 11, wherein the sub-blockclassification is based on AC projections associated with thesub-blocks.
 14. The computer system of claim 11, wherein the blockclassification is based on projections associated with the sub-blockswithout DC removal.
 15. A non-transitory computer readable medium havingstored thereon a computer program for video coding, the computer programconfigured to cause one or more computer processors to: receive videodata comprising sub-blocks corresponding to neighborhood data; an ACprojection of each sub-block of the sub-blocks at an angle by removing aDC component from a sub-block and summing pixel values along in a lineoriented at the angle; aggregate AC projections of the sub-blocks in thesame direction that is orthogonal to the angle, by summing absolutevalues of the AC projections in the same direction; compute AC energyindices and directionality indices based on the aggregated ACprojections in a plurality of directions, an AC energy index at a givenangle being based on aggregated AC projections in a direction that isorthogonal to the given angle; compute a class index based on thecomputed AC energy and directionality indices; and decode the video databased on the computed class index.
 16. The computer readable medium ofclaim 15, wherein a number of the directionality indices is the same asa number of the plurality of directions and a non-directional class. 17.The computer readable medium of claim 16, wherein a number of AC energyindices corresponds to the number of directions.
 18. The computerreadable medium of claim 15, wherein the computer program is furtherconfigured to cause one or more computer processors to classify thesub-blocks based on an on/off class for filtering of the sub-block basedon the directional AC energy indicators.
 19. The computer readablemedium of claim 18, wherein the sub-block classification is based on aratio of horizontal and vertical directional AC energy indices and/or aratio of 45-degree and 135-degree directional AC energy indicators. 20.The computer readable medium of claim 18, wherein the sub-blockclassification is based on AC projections associated with thesub-blocks.